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Birdbuddy in the classroom: leveraging AI-powered bird feeders for undergraduate biology education
Manuela Tripepi1, Jason Yang1, Daud Tariq1
1Department of Biological and Chemical Sciences, College of Life Sciences, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
Abstract:
Artificial intelligence (AI) rapidly transforms biological research and STEM education by enabling automated data collection and analysis. In order to teach students about biodiversity monitoring, data validation, and the importance of human oversight in machine learning, we created an activity utilizing Birdbuddy, a commercially available AI-enabled bird feeder. Students set up feeders in their local surroundings, gather automatically produced photos and species identifications, and verify the data collected to assess the accuracy of AI outputs. The activities promote conversation on AI bias and inaccuracy while highlighting transferable skills like ecological analysis, spreadsheet management, and experimental design. Birdbuddy encourages use in undergraduate classes, K-12 partnerships, and community science projects due to its low cost, portability, and ease of maintenance. In addition to promoting inclusive, experiential learning and developing an appreciation for biodiversity and the scientific method, this technology offers a scalable, affordable way to connect ecological research with AI literacy.
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